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Time Series Forecasting
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Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).
<span style="color:grey; opacity: 0.6">( Image credit: ThaiBinh Nguyen )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks
The Relevance of AWS Chronos: An Evaluation of Standard Methods for Time Series Forecasting with Limited Tuning
STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via
Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin
Towards Lightweight Time Series Forecasting: a Patch-wise Transformer with Weak Data Enriching
Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Tasks
Navigating Tomorrow: Reliably Assessing Large Language Models Performance on Future Event Prediction
Using Pre-trained LLMs for Multivariate Time Series Forecasting
Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
DCIts -- Deep Convolutional Interpreter for time series
Sequence Complementor: Complementing Transformers For Time Series Forecasting with Learnable Sequences
The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features
Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting
Time Series Language Model for Descriptive Caption Generation
Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes
Surrogate Modeling for Explainable Predictive Time Series Corrections
Hidformer: Transformer-Style Neural Network in Stock Price Forecasting
Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable Multivariate Time Series Forecasting
Neural Conformal Control for Time Series Forecasting
Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning
xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition
Quantum Time-Series Learning with Evolutionary Algorithms
EasyTime: Time Series Forecasting Made Easy
WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting
Breaking the Context Bottleneck on Long Time Series Forecasting
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment
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